Editore: World Scientific Pub Co Inc, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: Isaiah Thomas Books & Prints, Inc., Cotuit, MA, U.S.A.
EUR 31,81
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Aggiungi al carrelloHardcover. Condizione: Fine. Fine new copy in dj. Review slip from publisher laid in. sci; Series In Machine Perception And Artificial Intelligence; 9.0 X 6.2 X 0.9 inches; 235 pages.
Editore: World Scientific Publishing Co Pte Ltd, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: Ammareal, Morangis, Francia
Hardcover. Condizione: Très bon. Ancien livre de bibliothèque. Edition 2005. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 2005. Ammareal gives back up to 15% of this item's net price to charity organizations.
Editore: World Scientific Publishing Company, Incorporated, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: Books Puddle, New York, NY, U.S.A.
EUR 129,49
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Aggiungi al carrelloCondizione: New. pp. 248.
Editore: World Scientific Publishing Company, Incorporated, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: Majestic Books, Hounslow, Regno Unito
EUR 133,87
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Aggiungi al carrelloCondizione: New. pp. 248.
Editore: World Scientific Publishing Company, Incorporated, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 135,49
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Aggiungi al carrelloCondizione: New. pp. 248.
Editore: World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 151,26
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Aggiungi al carrelloCondizione: New.
Editore: World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 150,99
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Aggiungi al carrelloCondizione: New.
Editore: WORLD SCIENTIFIC PUB CO INC, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 168,87
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Aggiungi al carrelloCondizione: New. Describes opportunities for utilizing robust graph representations of data with machine learning algorithms. The authors have selected the domain of web content mining, which involves the clustering and classification of web documents based on their textual.
Editore: World Scientific Pub Co Inc, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 173,39
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Aggiungi al carrelloHardcover. Condizione: Brand New. illustrated edition. 248 pages. 9.25x6.25x0.75 inches. In Stock.
Editore: World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 171,28
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Editore: World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 173,04
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Editore: World Scientific Publishing Company, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 201,85
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Aggiungi al carrelloCondizione: New. In.
Editore: World Scientific Publishing Company Mai 2005, 2005
ISBN 10: 9812563393 ISBN 13: 9789812563392
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 206,36
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware - This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors. Through the use of graph distance -- a relatively new approach for determining graph similarity -- the authors show how well-known algorithms, such as k-means clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization of additional information found in graph representations, while at the same time employing well-known, proven algorithms. To demonstrate and investigate these novel techniques, the authors have selected the domain of web content mining, which involves the clustering and classification of web documents based on their textual substance. Several methods of representing web document content by graphs are introduced; an interesting feature of these representations is that they allow for a polynomial time distance computation, something which is typically an NP-complete problem when using graphs. Experimental results are reported for both clustering and classification in three web document collections using a variety of graph representations, distance measures, and algorithm parameters. In addition, this book describes several other related topics, many of which provide excellent starting points for researchers and students interested in exploring this new area of machine learning further. These topics include creating graph-based multiple classifier ensembles through random node selection and visualization of graph-based data usingmultidimensional scaling.